{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Plot randomly generated classification dataset\n\n\nPlot several randomly generated 2D classification datasets.\nThis example illustrates the :func:`datasets.make_classification`\n:func:`datasets.make_blobs` and :func:`datasets.make_gaussian_quantiles`\nfunctions.\n\nFor ``make_classification``, three binary and two multi-class classification\ndatasets are generated, with different numbers of informative features and\nclusters per class.  \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "print(__doc__)\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import make_classification\nfrom sklearn.datasets import make_blobs\nfrom sklearn.datasets import make_gaussian_quantiles\n\nplt.figure(figsize=(8, 8))\nplt.subplots_adjust(bottom=.05, top=.9, left=.05, right=.95)\n\nplt.subplot(321)\nplt.title(\"One informative feature, one cluster per class\", fontsize='small')\nX1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=1,\n                             n_clusters_per_class=1)\nplt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,\n            s=25, edgecolor='k')\n\nplt.subplot(322)\nplt.title(\"Two informative features, one cluster per class\", fontsize='small')\nX1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,\n                             n_clusters_per_class=1)\nplt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,\n            s=25, edgecolor='k')\n\nplt.subplot(323)\nplt.title(\"Two informative features, two clusters per class\",\n          fontsize='small')\nX2, Y2 = make_classification(n_features=2, n_redundant=0, n_informative=2)\nplt.scatter(X2[:, 0], X2[:, 1], marker='o', c=Y2,\n            s=25, edgecolor='k')\n\nplt.subplot(324)\nplt.title(\"Multi-class, two informative features, one cluster\",\n          fontsize='small')\nX1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,\n                             n_clusters_per_class=1, n_classes=3)\nplt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,\n            s=25, edgecolor='k')\n\nplt.subplot(325)\nplt.title(\"Three blobs\", fontsize='small')\nX1, Y1 = make_blobs(n_features=2, centers=3)\nplt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,\n            s=25, edgecolor='k')\n\nplt.subplot(326)\nplt.title(\"Gaussian divided into three quantiles\", fontsize='small')\nX1, Y1 = make_gaussian_quantiles(n_features=2, n_classes=3)\nplt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,\n            s=25, edgecolor='k')\n\nplt.show()"
      ]
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